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UID:5511@i2m.univ-amu.fr
DTSTART;TZID=Europe/Paris:20130627T140000
DTEND;TZID=Europe/Paris:20130627T150000
DTSTAMP:20241029T171752Z
URL:https://www.i2m.univ-amu.fr/evenements/s-barbieri-latp-optimal-time-fr
 equency-bases-for-eeg-signal-classification-in-the-context-of-bci/
SUMMARY: (...): S. Barbieri (LATP): Optimal Time-Frequency Bases for EEG Si
 gnal Classification in the Context of BCI.
DESCRIPTION:: Optimal Time-Frequency Bases for EEG Signal Classification in
  the Context of BCI. by Sebastiano Barbieri\n\nAbstract:\nWe consider the 
 problem of classifying multi-sensor signals\\\, more\nprecisely EEG signal
 s in the context of Brain Computer Interfaces (BCI)\\\, by selection of\nt
 ime-frequency features. The features are determined among local cosine bas
 es (MDCT)\nby a ``best basis'' type algorithm adapted to the classificatio
 n context.\n\nIn the BCI domain\\\, or more generally in neuroscience\\\, 
 many classification algorithms\nare based upon automatic approaches (artif
 icial neural networks\\\, SVM\\\, ...) which do not\nallow a simple interp
 retation of the features. In the talk we propose an approach which allows 
 such interpretation\\\, since the features are determined in the form of t
 ime-frequency atoms\\\, similarly to classic analyses of EEG signals which
  involve specific frequency\nbands and time intervals.\n\nThe proposed alg
 orithm generalizes the best discriminant basis algorithm\nby Saito\\\, emp
 loying pairwise comparisons between the signals belonging to two\nclasses 
 of data. Results on artificial data show that the method is able to determ
 ine\nsimulated differences between signals. Results on real data are compe
 titive with state of\nthe art classification algorithms and more easily in
 terpretable.
CATEGORIES:Séminaire,Signal et Apprentissage
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